About this Research Topic
Imaging is the visual representation of structures and/or functions of objects, such as biological molecules, biological ultrastructures, tissues, and the spatial organizations of the objects. It is also an indispensable step towards diagnostics and therapeutics in modern medicine. For example, during the current pandemic caused by COVID-19 CT-scans have been used, in addition to nucleic acid detection, as a main criterion for diagnostics. Unlike computers, the human brain has a remarkably strong ability to understand and interpret the information obtained from imaging data, more so than from interpreting numerical or textual data.
Imaging is playing an increasingly significant role in both biological and biomedical sciences, with technologies including optical microscopy, fluorescence microscopy, electron tomography, nuclear magnetic resonance, single particle cryo-EM, and X-ray crystallography. They have provided rich information about biological systems and molecules at various resolutions, all the way from tissue-level, to cellular-level, to organelle-level, to macromolecular-level, to small-molecular-level, and to atomic-level. Imaging also has many diagnostic and therapeutic applications, such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and optical coherence tomography (OCT). Such technologies can provide fast, non-invasive, painless, and direct information to clinicians and physicians, which is critical to not only diagnosis, but also prognosis and treatment.
With the recent development of AI technologies, especially deep learning, the frontiers on biological and biomedical imaging have been greatly advanced. In this Research Topic, we are seeking high quality works on developing or applying state-of-the-art AI techniques for processing, information mining, integrating, diagnosing, comparing, and reviewing biological and biomedical imaging, with their applications in biology, diagnostics and therapeutics. Some example topics are listed below:
• Denoising or super-resolution of biological or biomedical images
• Signal identification or alignment from biological or biomedical images
• Feature extraction from biological or biomedical images
• Structure reconstruction from biological or biomedical images
• Classification of biomedical images with applications in diagnosis and prognosis
• Segmentation and quantification from biological or biomedical images
• Integration of multiple modalities of imaging data
• Prediction of prognosis and treatment from imaging data
• Quantitative methods for molecular diagnostic and therapeutic imaging
• Image-omics and radiomics in disease diagnosis and therapy
This Research Topic is open to different types of contributions such as original research papers, reviews, perspectives, and all other article types supported by the publisher (please find a full list including descriptions here). Submitting an abstract is not a prerequisite for submitting a manuscript.
Doctors Gao and Li hold patents related to artificial intelligence.
Imaging is playing an increasingly significant role in both biological and biomedical sciences, with technologies including optical microscopy, fluorescence microscopy, electron tomography, nuclear magnetic resonance, single particle cryo-EM, and X-ray crystallography. They have provided rich information about biological systems and molecules at various resolutions, all the way from tissue-level, to cellular-level, to organelle-level, to macromolecular-level, to small-molecular-level, and to atomic-level. Imaging also has many diagnostic and therapeutic applications, such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and optical coherence tomography (OCT). Such technologies can provide fast, non-invasive, painless, and direct information to clinicians and physicians, which is critical to not only diagnosis, but also prognosis and treatment.
With the recent development of AI technologies, especially deep learning, the frontiers on biological and biomedical imaging have been greatly advanced. In this Research Topic, we are seeking high quality works on developing or applying state-of-the-art AI techniques for processing, information mining, integrating, diagnosing, comparing, and reviewing biological and biomedical imaging, with their applications in biology, diagnostics and therapeutics. Some example topics are listed below:
• Denoising or super-resolution of biological or biomedical images
• Signal identification or alignment from biological or biomedical images
• Feature extraction from biological or biomedical images
• Structure reconstruction from biological or biomedical images
• Classification of biomedical images with applications in diagnosis and prognosis
• Segmentation and quantification from biological or biomedical images
• Integration of multiple modalities of imaging data
• Prediction of prognosis and treatment from imaging data
• Quantitative methods for molecular diagnostic and therapeutic imaging
• Image-omics and radiomics in disease diagnosis and therapy
This Research Topic is open to different types of contributions such as original research papers, reviews, perspectives, and all other article types supported by the publisher (please find a full list including descriptions here). Submitting an abstract is not a prerequisite for submitting a manuscript.
Doctors Gao and Li hold patents related to artificial intelligence.
Keywords: Artificial intelligence, biological imaging, biomedical imaging, deep learning, image processing
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